1 code implementation • 7 Nov 2016 • Nicolas Goix, Nicolas Drougard, Romain Brault, Maël Chiapino
Random Forests (RFs) are strong machine learning tools for classification and regression.
2 code implementations • 5 Jul 2016 • Nicolas Goix
When sufficient labeled data are available, classical criteria based on Receiver Operating Characteristic (ROC) or Precision-Recall (PR) curves can be used to compare the performance of un-supervised anomaly detection algorithms.
no code implementations • 31 Mar 2016 • Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Extremes play a special role in Anomaly Detection.
no code implementations • 21 Jul 2015 • Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Capturing the dependence structure of multivariate extreme events is a major concern in many fields involving the management of risks stemming from multiple sources, e. g. portfolio monitoring, insurance, environmental risk management and anomaly detection.
no code implementations • 5 Feb 2015 • Nicolas Goix, Anne Sabourin, Stéphan Clémençon
Extensions to the multivariate setting are far from straightforward and it is precisely the main purpose of this paper to introduce a novel and convenient (functional) criterion for measuring the performance of a scoring function regarding the anomaly ranking task, referred to as the Excess-Mass curve (EM curve).